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Record W2260459330 · doi:10.1021/acsami.5b08782

Fluorescent N-Doped Carbon Dots as <i>in Vitro</i> and <i>in Vivo</i> Nanothermometer

2015· article· en· W2260459330 on OpenAlexaff
Yanmei Yang, Weiqiang Kong, Hao Li, Juan Liu, Manman Yang, Hui Huang, Yang Liu, Zhongyang Wang, Zhiqiang Wang, Tsun‐Kong Sham, Jun Zhong, Chao Wang, Zhuang Liu, Shuit‐Tong Lee, Zhenhui Kang

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCarbon and Quantum Dots Applications
Canadian institutionsCanadian Light Source (Canada)
FundersCollaborative Innovation Center of Suzhou Nano Science and TechnologyPriority Academic Program Development of Jiangsu Higher Education InstitutionsSuzhou University of Science and TechnologyMinistry of Science and Technology of the People's Republic of ChinaMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsFluorescenceMaterials scienceIn vivoCarbon fibersIonic bondingDopingPhotochemistryHydrogen bondAnalytical Chemistry (journal)NanotechnologyMoleculeOptoelectronicsIonOpticsChemistryOrganic chemistryBiology

Abstract

fetched live from OpenAlex

The fluorescent N-doped carbon dots (N-CDs) obtained from C3N4 emit strong blue fluorescence, which is stable with different ionic strengths and time. The fluorescence intensity of N-CDs decreases with the temperature increasing, while it can recover to the initial one with the temperature decreasing. It is an accurate linear response of fluorescence intensity to temperature, which may be attributed to the synergistic effect of abundant oxygen-containing functional groups and hydrogen bonds. Further experiments also demonstrate that N-CDs can serve as effective in vitro and in vivo fluorescence-based nanothermometer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.248
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations154
Published2015
Admission routes1
Has abstractyes

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